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On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming

Andreas Wächter (andreasw***at***watson.ibm.com)
Lorenz T Biegler (lb01***at***andrew.cmu.edu)

Abstract: We present a primal-dual interior-point algorithm with a filter line-search method for nonlinear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix. Heuristics are also considered that allow faster performance. This method has been implemented in the IPOPT code, which we demonstrate in a detailed numerical study based on 954 problems from the CUTEr test set. An evaluation is made of several line-search options, and a comparison is provided with two state-of-the-art interior-point codes for nonlinear programming.

Keywords: nonlinear programming -- nonconvex constrained optimization -- filter method -- line search -- interior-point method -- barrier method

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: IBM Research Report RC 23149 IBM T.J. Watson Research Center Yorktown Heights, NY. USA March 12, 2004

Download: [Postscript][Compressed Postscript][PDF]

Entry Submitted: 03/15/2004
Entry Accepted: 03/15/2004
Entry Last Modified: 03/19/2004

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